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@article{205240,
        author = {Shreyas Malusare and Hrushikesh Kolhe and Priyanka Belapurkar and Prof. Ulka Bansode},
        title = {AI-Enhanced Web Vulnerability Scanner: Integrating Dynamic Payload Generation and Machine Learning-Based Risk Assessment},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5886-5891},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205240},
        abstract = {Modern web ecosystems confront an unprecedented surge in sophisticated cyber threats, predominantly characterized by multi-vector injection flaws, cross-site scripting, and security misconfigurations. While automated Dynamic Application Security Testing (DAST) utilities are routinely deployed to audit web surfaces, conventional black-box scanners suffer from significant operational bottlenecks. These tools rely heavily on static payload dictionaries, yield substantial false-positive rates, and possess a fundamental blindness to business context—reporting technical flaw severity via static scores while failing to evaluate the operational impact on the targeted asset. To bridge this critical gap, this research introduces an intelligent, web-based automated vulnerability scanner. The proposed architecture integrates an asynchronous reconnaissance engine with an environment-aware fuzzer, replacing generic wordlists with context-prioritized payload execution. Furthermore, an advanced machine learning classification module evaluates the contextual architecture of discovered vulnerable endpoints to dynamically predict the asset's business value and magnitude of impact.},
        keywords = {Dynamic Application Security Testing, Machine Learning, Risk Assessment, Vulnerability Scanning, Web Security.},
        month = {June},
        }